Detection of periodontal bone loss in mandibular area from dental panoramic radiograph using image processing techniques

Abstract

Medical imaging plays the vital role in diagnosis of abnormalities. Periodontitis is a common chronic inflammatory disease damaging the soft tissue that untreated can lead to loss of bone, which supports the teeth. The severity of periodontitis is correlated to pocket depth in alveolar area. Even if analyses of the pocket depth can be manually performed, an automatic assistive tool can drastically help radiologists conduct more accurate analyses. In the proposed research, image processing algorithms were developed to compute pocket depth and diagnose the periodontitis. Radiologists manually segmented 350 panoramic radiographic images, dividing them into normal and periodontitis. The same dataset is used in our work to validate the Classification algorithm. The images are preprocessed with median filter and histogram equalization to improve the contrast and then segmented using two‐dimensional‐Otsu thresholding method into teeth and bony area in the mandibular region. Normal pocket depth of 3 mm as reported by American Academy of Periodontology is equivalently converted to pixel height in the radiographic images. Based on this pocket depth rule based classification method classifies the images into normal and periodontitis. The proposed work achieved 91.34% accuracy, 92.8% sensitivity, and 95.47% F‐score in classifying the dental panoramic radiography.

Development and validation of an artificial intelligence software for periodontal bone loss in panoramic imaging

“…Performance metrics of the model are reported as an accuracy of about 91.34%, the sensitivity of 92.8%, and Fscore of 95.47%. 15 Researchers also developed another model to diagnose periodontitis stages of mandibular area using Adaptive Center Line-Distance Based approach. The proposed model achieved an accuracy of 92.83%, precision of 97.59%, recall/sensitivity of 95.01% and F-score/F-measure of 96.28%.…”

“…Teeth and bony shapes and structures are obtained using erosion morphology operation, and located by two‐dimensional Otsu (2D Otsu) threshold segmentation. Performance metrics of the model are reported as an accuracy of about 91.34%, the sensitivity of 92.8%, and F ‐score of 95.47% 15 . Researchers also developed another model to diagnose periodontitis stages of mandibular area using Adaptive Center Line‐Distance Based approach.…”

“…AI techniques can be used in developing systems for the diagnosis of periodontitis. Several studies on the diagnosis of periodontal disease in panoramic images have been reported, and the CNN approach is commonly adopted in the developed models 7,15–19 . This study aims to evaluate the performance of a web‐based AI software (DiagnoCat) developed for tooth numbering and diagnosis of periodontal defects on digital panoramic radiographs, by comparing the results of the AI software with the hybrid results of human observers.…”

“…Several studies on the diagnosis of periodontal disease in panoramic images have been reported, and the CNN approach is commonly adopted in the developed models. 7,[15][16][17][18][19] This study aims to evaluate the performance of a web-based AI software (DiagnoCat) developed for tooth numbering and diagnosis of periodontal defects on digital panoramic radiographs, by comparing the results of the AI software with the hybrid results of human observers. The null hypothesis of this research is that the results of the AI system are comparable to the consensus determined by the voting of human observers for detecting periodontal bone loss in panoramic radiographs.…”

Automated Assessment of Radiographic Bone Loss in the Posterior Maxilla Utilizing a Multi-Object Detection Artificial Intelligence Algorithm

“…The use of two-dimensional radiographic images is a common diagnostic tool in the assessment of periodontitis [13], a disease that results in the loss of periodontal attachment, and consequently, visible bone loss. The degree of radiographic bone loss can be used to assess the course of therapy or the severity of the disease [14]. It is necessary to initiate or adjust treatment if the bone loss exceeds a certain level within a specified period of time.…”